Background. Thrombospondin type 1 domain-containing 7A (THSD7A) was reported to play a procancer role in esophageal squamous cell carcinoma (ESCC). The aim of the study was to screen the downstream functional genes of THSD7A and explore their functions in ESCC, based on the reported research into THSD7A function and on gene microarrays. Methods. We adopted quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and Celigo high-content screening (HCS) technology to screen the downstream genes of THSD7A. The expression level of target genes was examined by PCR, western blot, and immunohistochemistry (IHC). The effects of these target genes on ESCC malignant biological behavior were performed in vivo and in vitro. The Kaplan-Meier (K-M) survival analysis and Cox regression were used to analyze the prognostic significance of target genes in ESCC patients. Experiments in the literature on liver cancer (LC) were repeated to verify the functions of these genes in different tumors. We further explored the cancer-promoting mechanism of target genes in ESCC by sequencing of the genes’ exons. Results. Scavenger receptor class A member 5 (SCARA5) was proved to be the downstream driving gene of THSD7A. SCARA5 promoted cell proliferation and migration but inhibited apoptosis in ESCC. IHC results confirmed that SCARA5 expression in ESCC exceeded that in normal tissues. The K-M survival analysis indicated that SCARA5 expression quantity was not related to prognosis, but tumor volume and T classification were both the independent prognostic factors. Repetition of experiments in LC in the literature confirmed that SCARA5 had exactly opposite functions in EC and LC. Conclusion. SCARA5 was related to the development and occurrence of ESCC. Our findings suggested that it was a potentially diagnostic individualized therapeutic target for ESCC in the future and that its application could possibly be combined with that of upstream THSD7A gene.

We aimed at investigating the implication of ATP6V0C in esophageal cancer (ECa) and exploring how ATP6V0C participates in this process. ATP6V0C expression in 46 pairs of ECa tissues was determined by RT-PCR analysis, and the relationship between ATP6V0C and clinicopathological indicators as wells as prognosis of ECa patients was analyzed. Results demonstrated that ATP6V0C was significantly increased in ECa.Participants with high- ATP6V0C exhibited markedly higher incidence of metastasis and shorter survival rates. Inhibition of ATP6V0C attenuated cell invasive and metastasis abilities. Meanwhile, Luciferase assay confirmed the binding between ATP6V0C and TWF1. TWF1 expression showed an increase in ECa cell lines and tissues, which was positively correlated with ATP6V0C level. In addition, we found that overexpression of TWF1 counteracted the effects of knockdown of ATP6V0C on the proliferation and migration of ECa, and thus affect the malignant progression of ECa. ATP6V0C and TWF1 are both highly expressed and positively correlated in tumor tissues of ECa patients. In addition, ATP6V0C accelerated the malignant progression of ECa cells through interacting with TWF1.

BackgroundLung adenocarcinoma(LUAD) is the most prevalent subtype of lung cancer today. There is a close relationship between Anoikis related genes(ARGs) and tumor prognosis, drug susceptibility, and tumor microenvironment(TME).MethodWe calculated differential expression genes using downloaded Anoikis genes and selected genes of prognostic value. Consensus clustering analysis was used and characterized between different clusters. Differences between the different groups were also explored. Risk scores and Nomogram with predictive prognostic functions were established. Immune status and drug sensitivity were also assessed between different risk groups. Single-cell data were downloaded to compare the expression profiles of selected genes, and immunohistochemical results of selected genes were also downloaded to corroborate the reliability of the manuscript.ResultTwo clusters were identified on the basis of related gene expression. We analyzed the survival time, functional enrichment between the two groups and found significant differences between the two clusters. Significant relationships were found between the different clusters and clinical variables. group B had a significantly lower KM curve than group A, as well as a significant enrichment in multiple tumor functions. A risk score with prognostic value was established. The risk score was found to have a high predictive value for prognosis and was an independent prognostic factor. Combined with clinical variables, a Nomogram was established and found to be an accurate predictor of patient prognosis. There were significant differences in immune status between the different risk groups. Patients in the low-risk group were significantly better treated than those in the high-risk group. Finally single cell data confirmed the expression of the selected genes. Also, the immunohistochemical results helped us to confirm the selected genes have increased expression in tumor tissue.ConclusionIn conclusion, this paper reveals the role of ARGs and immune status, drug susceptibility, and prediction of prognosis in LUAD. Also, an accurate prognostic prediction model was established based on genetic.

Background Adenocarcinoma of the lung is the most common pathological subtype. This paper is to investigate the relationship between endoplasmic reticulum-related LncRNA and prognosis of patients with lung adenocarcinomaMethod A total 295 ER stress-related genes are downloaded from the GSEA website and the TCGA website was obtained for TCGA-LUAD patient profiles and mutation data. Co-expression analysis, the univariate cox regression and Least Absolute Shrinkage and Selection Operator Cox regression was performed on significantly expressed LncRNAs. Next, a risk model was developed using multifactorial analysis to classify patients into different risk groups. Survival analysis and nomogram were also constructed and evaluated for patients in different groups. Finally, the tumor microenvironment and immune landscape were explored.Result 14 genes found to be strongly associated with patient prognosis. Risk score models were created and patients were grouped using risk score. Clinical variables and signature were also used to build the nomogram with perfect prediction function. The AUC value for 1,3,5 are respectively 0.739,0.734,0.753. The human immune response and Neutrophil extracellular trap formation are related to signature. Immune responses, immune cells, and immunological checkpoints were found to be considerably different. Last, We screened for sensitive drugs.Conclusion Collectively, we found that 14 ERS-related Lncrna signature was meaningful in predicting patient response to immunotherapy.